




'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """layoutlmv3"""







   def __init__(						self   ,				UpperCamelCase=5_0265   ,				UpperCamelCase=768   ,				UpperCamelCase=12   ,				UpperCamelCase=12   ,				UpperCamelCase=3072   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=512   ,				UpperCamelCase=2   ,				UpperCamelCase=0.02   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=1   ,				UpperCamelCase=0   ,				UpperCamelCase=2   ,				UpperCamelCase=1024   ,				UpperCamelCase=128   ,				UpperCamelCase=128   ,				UpperCamelCase=True   ,				UpperCamelCase=32   ,				UpperCamelCase=128   ,				UpperCamelCase=64   ,				UpperCamelCase=256   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=224   ,				UpperCamelCase=3   ,				UpperCamelCase=16   ,				UpperCamelCase=None   ,				**UpperCamelCase   ,				)					->     Optional[int]:
     super().__init__(
         vocab_size=UpperCamelCase   ,				hidden_size=UpperCamelCase   ,				num_hidden_layers=UpperCamelCase   ,				num_attention_heads=UpperCamelCase   ,				intermediate_size=UpperCamelCase   ,				hidden_act=UpperCamelCase   ,				hidden_dropout_prob=UpperCamelCase   ,				attention_probs_dropout_prob=UpperCamelCase   ,				max_position_embeddings=UpperCamelCase   ,				type_vocab_size=UpperCamelCase   ,				initializer_range=UpperCamelCase   ,				layer_norm_eps=UpperCamelCase   ,				pad_token_id=UpperCamelCase   ,				bos_token_id=UpperCamelCase   ,				eos_token_id=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       max_ad_position_embeddings
     __a								=       coordinate_size
     __a								=       shape_size
     __a								=       has_relative_attention_bias
     __a								=       rel_pos_bins
     __a								=       max_rel_pos
     __a								=       has_spatial_attention_bias
     __a								=       rel_ad_pos_bins
     __a								=       max_rel_ad_pos
     __a								=       text_embed
     __a								=       visual_embed
     __a								=       input_size
     __a								=       num_channels
     __a								=       patch_size
     __a								=       classifier_dropout





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.12"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     # The order of inputs is different for question answering and sequence classification
     if self.task in ["question-answering", "sequence-classification"]:
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
               ('bbox', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]   )
     else:
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('bbox', {0: 'batch', 1: 'sequence'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels'}),
           ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-5







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = -1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 40   ,				UpperCamelCase = 40   ,				)					->     Mapping[str, Any]:
     setattr(processor.image_processor   ,				'apply_ocr'   ,				UpperCamelCase   )

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     __a								=       compute_effective_axis_dimension(
         UpperCamelCase   ,				fixed_dimension=OnnxConfig.default_fixed_batch   ,				num_token_to_add=0   )
     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     __a								=       processor.tokenizer.num_special_tokens_to_add(UpperCamelCase   )
     __a								=       compute_effective_axis_dimension(
         UpperCamelCase   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=UpperCamelCase   )
     # Generate dummy inputs according to compute batch and sequence
     __a								=       [[' '.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     __a								=       [[[48, 84, 73, 128]]] * batch_size

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )

     __a								=       dict(
         processor(
             UpperCamelCase   ,				text=UpperCamelCase   ,				boxes=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				)   )

     return inputs









'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



